A Novel Criterion of Wavelet Packet Best Basis Selection for Signal Classification With Application to Brain–Computer Interfaces

Vautrin, Denis; Artusi, Xavier; Lucas, Marie-Françoise; Farina, Dario

doi:10.1109/tbme.2009.2028014

Cited by 19 publications

(10 citation statements)

References 6 publications

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“…Such signals can play an important role for developing novel BCI since they provide information about movements. The dataset we use has already been analyzed in a recent study [38], while investigation on similar data has also been carried out by Farina et al [11].…”

Section: Bci Datasetmentioning

confidence: 99%

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Wavelet kernel learning

Yger

Rakotomamonjy²

2011

Pattern Recognition

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Section: Bci Datasetmentioning

confidence: 99%

“…A short description of the dataset follows and for more details the readers are referred to the works of Vautrin et al [38]. The EEG activity of a subject was recorded at 32 standard positions using tin electrodes mounted in a cap.…”

Section: Bci Datasetmentioning

confidence: 99%

Wavelet kernel learning

Yger

Rakotomamonjy²

2011

Pattern Recognition

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“…At the end of these procedures, analyzed signals can be represented by as few and large coefficients as possible [10,23]. This adaptive and sparse decomposition was used previously in a wide range of problems such as signal analysis, filtering, or compression [9,15,24,33].…”

Section: Introductionmentioning

confidence: 99%

Directional dual-tree complex wavelet packet transforms for processing quadrature signals

Serbes

Gülçür

Aydin

2014

Med Biol Eng Comput

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Quadrature signals containing in-phase and quadrature-phase components are used in many signal processing applications in every field of science and engineering. Specifically, Doppler ultrasound systems used to evaluate cardiovascular disorders noninvasively also result in quadrature format signals. In order to obtain directional blood flow information, the quadrature outputs have to be preprocessed using methods such as asymmetrical and symmetrical phasing filter techniques. These resultant directional signals can be employed in order to detect asymptomatic embolic signals caused by small emboli, which are indicators of a possible future stroke, in the cerebral circulation. Various transform-based methods such as Fourier and wavelet were frequently used in processing embolic signals. However, most of the times, the Fourier and discrete wavelet transforms are not appropriate for the analysis of embolic signals due to their non-stationary time-frequency behavior. Alternatively, discrete wavelet packet transform can perform an adaptive decomposition of the time-frequency axis. In this study, directional discrete wavelet packet transforms, which have the ability to map directional information while processing quadrature signals and have less computational complexity than the existing wavelet packet-based methods, are introduced. The performances of proposed methods are examined in detail by using single-frequency, synthetic narrow-band, and embolic quadrature signals.

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“…They are then classified using decision functions learned on the training set composed of labeled signals. The classification performance depends on the choice of the pre processing techniques (Vautrin et al, 2009). A large training session becomes beneficial to lay down the decision rules that allow the classification of the user's intention (Birbaumer et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Classifying Single Trail Electroencephalogram Using Gaussian Smoothened Fast Hartley Transform for Brain Computer Interface during Motor Imagery

Deepa¹

2011

Journal of Computer Science

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Problem statement: Brain-Computer Interface (BCI) is a emerging research area which translates the brain signals for any motor related actions into computer understandable signals by capturing the signal, processing the signal and classifying the motor imagery. This area of work finds various applications in neuroprosthetics. Mental activity leads to changes of electrophysiological signals like the Electroencephalogram (EEG) or Electrocorticogram (ECoG). Approach: The BCI system detects such changes and transforms it into a control signal which can, for example, be used as to control a electric wheel. In this study the BCI paradigm is tested by our proposed Gaussian smoothened Fast Hartley Transform (GS-FHT) which is used to compute the energies of different motor imageries the subject thinks after selecting the required frequencies using band pass filter. Results: We apply this procedure to BCI Competition dataset IVA, a publicly available EEG repository. Conclusion: The evaluations of preprocessed signals showed that the extracted features were interpretable and can lead to high classification accuracy by various mining algorithms.

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A Novel Criterion of Wavelet Packet Best Basis Selection for Signal Classification With Application to Brain–Computer Interfaces

Cited by 19 publications

References 6 publications

Wavelet kernel learning

Wavelet kernel learning

Directional dual-tree complex wavelet packet transforms for processing quadrature signals

Classifying Single Trail Electroencephalogram Using Gaussian Smoothened Fast Hartley Transform for Brain Computer Interface during Motor Imagery

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